WildEar-HZ strict-005 is a lightweight bioacoustic post-training package. It does not redistribute BioLingual base weights. It contains:
21 target-species prototypes;
21 text prototypes;
eight non-target-animal prototypes;
dual Unknown thresholds;
fixed classification, cross-validation, and independent open-set reports;
replayable training and evaluation scripts.
At runtime, the frozen davidrrobinson/BioLingual encoder produces normalized 512-dimensional audio embeddings.
Input and Output
Input: 48 kHz mono audio;
windows: 5 seconds with a 2.5-second hop;
maximum duration: 60 seconds;
output: three ranked candidates from 21 target classes, model-reference time segments, and an Unknown risk signal.
Displayed candidate scores are temperature-normalized relative scores, not calibrated posterior probabilities.
Training
Licensed weak-label audio is globally separated by uploader/source group. High text-margin windows form class prototypes:
p_c = normalize(mean(z_i))
Explicit challenge references form adaptation prototypes:
p'_c = normalize((1 - alpha) p_c + alpha r_c)
alpha=0.15 is selected only on validation. Thirteen of 21 classes use audio-trained prototypes; eight use text fallback. Sixteen AI pseudo-label candidates fail the fixed-validation gate, so production uses none.
Final Source-Grouped Test
305 train / 111 validation / 88 test;
zero uploader-group overlap;
88 test recordings from 43 uploaders, covering 17 of 21 classes;
90.91% Accuracy;
81.43% Macro-F1;
95.45% top-three hit rate.
Nested Five-Fold Grouped Cross-Validation
Scope: 454 recordings from 13 classes with at least five source groups per class.
Accuracy: median +5.78 points, 95% interval +0.93 to +11.67;
Macro-F1: median +8.11 points, 95% interval +1.40 to +15.36.
Linear-head-versus-prototype intervals cross zero, so production retains prototypes.
Independent Open-Set Test
Non-target animals are globally separated by uploader; environmental audio is separated by original ESC fold. Prototype-train, calibration, and test overlaps are zero.
Group
AUROC
Online Known recall
Online FAR
Overall
0.9696
72.73%
6.45%
Non-target animal
0.9500
72.73%
20.00%
Environmental sound
0.9789
72.73%
0.00%
Unknown is a conservative risk signal, not an expert species identification.
Limitations
Final labels are platform recording-level weak labels, not expert event annotations.
Four classes are absent from final test.
Eight classes still use text fallback.
The main task is not formal multi-label event detection.
FAR is higher for non-target animals.
Do not use this model for enforcement, abundance estimates, or ecological-impact assessment.
